Bibliographic record
Abstract
ntroduction: Breast Cancer is the most frequently occurring cancer in Canadian women [1]. The standard of care normally involves breast conserving surgery and radiation therapy followed by breast reconstruction surgery. For successful breast reconstruction, the total volume loss must be accounted for. Unfortunately, the volume excised during surgery generally does not reflect total breast volume loss, for example, radiation therapy is known to cause volume loss of the breast [2]. Our goal is to provide the software and workflow necessary to calculate the breast volume using a non-invasive technique. By calculating and comparing the breast volume of the patient before undergoing reconstruction surgery to the baseline volume will help surgeon’s better estimate how much tissue needs to be replaced. Methods: A 3D surface scan of the patient’s chest is obtained. The scan is then imported into 3D Slicer where modules are used to isolate the target breast and calculate the volume . Results: The method provided to calculate breast volume is feasible using 3D Slicer and only requires one surface scan from the patient. The ground truth breast volume of the mannequin was 164mL with a standard deviation of 4.1ml (n=5). The volume of the mannequin’s breast was calculated using the workflow provided, the mean calculated volume was 160.8mL and the standard deviation was 4.7ml (n=4). Conclusion: Using a 3D surface scanner provides a non-invasive and quick way to calculate breast volume. This initial validation suggests this system may be accurate enough to aid the surgeon in the reconstruction process. References [1] Canadian Breast Cancer Society. (2015). Breast Cancer in Canada, 2015. Retrieved from https://www.cbcf.org/ontario/AboutBreastCancerMain/FactsStats/Pages/Breast-Cancer-Canada.asp [2] Haykal, Siba, Colin P. White, and Nicolas A. Guay. "An estimation of volume loss after radiation therapy on free flap breast reconstruction." Plastic and reconstructive surgery 131.6 (2013): 937e-939e.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".